Verification, the Key to AI (2001)

Summary

Verification, The Key to AI by Rich Sutton November 15, 2001 It is a bit unseemly for an AI researcher to claim to have a special insight or plan for how his field should proceed. If he has such, why doesn't he just pursue it and, if he is right, exhibit its special fruits? Without denying that, there is still a role for assessing and analyzing the field as a whole, for diagnosing the ills that repeatedly plague it, and to suggest general solutions. The insight that I would claim to have is that the key to a successful AI is that it can tell for itself whether or not it is working correctly. At one level this is a pragmatic issue. If the AI can't tell for itself whether it is working properly, then some person has to make that assessment and make any necessary modifications. An AI that can assess itself may be able to make the modifications itself. The Verification Principle: An AI system can create and maintain knowledge only to the extent that it can verify that knowledge itself. Successful verification occurs in all search-based AI systems, such as planners, game-players, even genetic algorithms. Deep Blue, for example, produces a score for each of its possible moves through an extensive search. Its belief that a particular move is a good one is verified by the search tree that shows its inevitable production of a good position. These systems don't have to be told what choices to make; they can tell for themselves. Image trying to program a chess machine by telling it what kinds of moves to make in each kind of position. Many early chess programs were constructed in this way. The problem, of course, was that there were many different kinds of chess positions. And the more advice and rules for move selection given by programmers, the more complex the system became and the more unexpected interactions there were between rules. The programs became brittle and unreliable, requiring constant maintainence, and before long this whole approach lost out to the "brute forc...